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Advanced Convolutional Neural Networks
# Display an image
def show(img):
plt.figure(figsize=(12,12))
plt.grid(False)
plt.axis('off')
plt.imshow(img)
# https://commons.wikimedia.org/wiki/File:Flickr_-_Nicholas_T_-_Big_#
# Sky_(1).jpg
url = 'https://storage.googleapis.com/applied-dl/clouds.jpg'
img = preprocess(download(url))
show(deprocess(img))
Now let's use the Inception pretrained network for extracting features. We use
several layers and the goal is to maximize their activations. tf.keras functional
API is our friend here:
# We'll maximize the activations of these layers
names = ['mixed2', 'mixed3', 'mixed4', 'mixed5']
layers = [inception_v3.get_layer(name).output for name in names]
# Create our feature extraction model
feat_extraction_model = tf.keras.Model(inputs=inception_v3.input,
outputs=layers)
def forward(img):
# Create a batch
img_batch = tf.expand_dims(img, axis=0)
# Forward the image through Inception, extract activations
# for the layers we selected above
return feat_extraction_model(img_batch)
The loss function is the mean of all the activation layers considered, normalized by
the number of units in the layer itself:
def calc_loss(layer_activations):
total_loss = 0
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